TAILIEUCHUNG - Fuzzy logic and T-test for load forecasting
This paper applied fuzzy rules to approximate the relationship between loads and other factors using the subtractive clustering. The implementation is carried out for one substation in Ho Chi Minh city. Results show that the proposed approach gives better accuracycy of forecasting, and the effort of finding crisp function for forecasting is not helping to have better results. | Journal of Science & Technology 131 (2018) 001-005 Fuzzy Logic and T-Test for Load Forecasting Phan Thi Thanh Binh1, Dinh Xuan Thu1, Vo Viet Cuong2,* 1 HCMC University of Technology, No. 268 Ly Thuong Kiet Street, District 10, HCMC, Vietnam HCMC University of Technology and Education, No. 1 Vo Van Ngan Street, HCMC, Vietnam Received: October 03, 2017; Accepted: November 26, 2018 2 Abstract The forecasting models based on regression function have the analytic form with proving that there is some rule expressing the correlation between forecasting value and other related fators. In reality, forecasted load is not always in linear form of factors, such as: temperature, population, GDP or historical load data. This paper applied fuzzy rules to approximate the relationship between loads and other factors using the subtractive clustering. The implementation is carried out for one substation in Ho Chi Minh city. Results show that the proposed approach gives better accuracycy of forecasting, and the effort of finding crisp function for forecasting is not helping to have better results. Keywords: subtractive clustering, fuzzy rule, correlation, T-test, load forecasting 1. Introduction* Their method is based on gridding the data space and computing a potential value for each grid point. Although this method is simple and effective, the computation grows exponentially with the dimension of the problem. Chiu [5] proposed an extension of Yager and Filev’s mountain method, called subtractive clustering, in which each data point, rather than the grid point, is considered as a potential cluster center. Using this method, the number of effective “grid points” to be evaluated is simply equal to the number of data points, independent of the dimension of the problem. By tradition, the forecasting models in regression function have an analytic form, such as Y = f(x1, x2, ., xn) or logY = f(logx1, logx2, ., logxn). These models are linear and are used only when the linear .
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